The authors thank Dr. Honghua Zhou for digital technology support.&#160;This study was supported by Capital&#39;s Funds for Health Improvement and Research of China&#160;(ID:2024-2-7031).

This study was approved by the Academic Ethics Committee of the Seventh Medical Center of Chinese PLA General Hospital in Beijing, China.
1. Participant inclusion/exclusion criteria
<ol>
	<li>Recruit aged participants 65 years or older and obtain their informed consent.</li>
	<li>Exclude participants who have obvious visual and lower limb disability, such as knee arthritis, thromboangiitis obliterans, and gout.</li>
</ol>
2. Preparation of the test area
<ol>
	<li>Set up the standardized iTUG test area of 5 m x 8 m or wider (see Figure 1) with 12 cameras distributed around the room. Put the traditional TUG setups into the test area: a chair and a sign to remind the participant to turn back. 
	NOTE: Signs indicating route lines are optional.</li>
	<li>Ensure that the motion capture area is limited to the scope of all the cameras and all the TUG test setup is non-light reflective.</li>
</ol>
3. Software preparation for the procedures before the test
<ol>
	<li>Install the motion capture software on the computer to be used in the room.</li>
	<li>Click the Seeker button to start the motion capture software. After starting the software, for General Camara Configuration, choose the default mode (frame rate = 60 frames/s, shutter speed = 1/1,000 s), which is acceptable for most situations. Set Selected Camera Settings and Camera Configuration Settings also in the default mode.</li>
	<li>Select Live mode to set the real-time settings. 
	NOTE: Post mode is used to analyze the data captured.</li>
	<li>Click on Markersets to configure the marker settings.</li>
	<li>Select XY axis in the Calibration of Ground Axis and millimeters in Calibration units.</li>
	<li>Click on Calibrate to select the calibration variation.</li>
	<li>Select Initial Calibration and click on the &#34;L&#34; shaped button. At the same time, take the &#34;L&#34; shaped calibrator into the field and put it in the center of the field to make sure it can be captured by all the cameras. 
	NOTE: The &#34;L&#34; shaped calibrator contains four light-reflective markers.</li>
	<li>Check on the screen whether all the 12 cameras can detect four reflective markers. If any camera detects &#8804;3 markers, reduce lightness and threshold at the left of the screen. If any camera detects &#8805;5 markers, check the field and clean or cover the unwanted reflective markers. 
	NOTE: Some tiny belongings of the former participant might be detected by the camera.</li>
	<li>Remove the &#34;L&#34; shaped calibrator from the field.</li>
	<li>Click on the &#34;T&#34; shaped button. At the same time, take the &#34;T&#34; shaped calibrator into the field and wave it to make sure it can be captured by all the cameras.</li>
	<li>Select Z axis in the Calibration of Up Axis and millimeters in Calibration units.</li>
	<li>Have the doctors check whether all the cameras can capture the markers. Wave the markers in each corner of the field, especially covering the possible space of the TUG test.</li>
	<li>Check in the screen whether all 12 cameras can detect the &#34;T&#34; shaped calibrator. If any camera cannot detect it, change the direction of the camera. 
	NOTE: There are two shapes of markers that need to be calibrated; &#34;L&#34; shaped and &#34;T&#34; shaped. The &#34;L&#34; shaped calibrator has four light-reflective points to calibrate the XY axis, and the &#34;T&#34; shaped calibrator has four light-reflective points to calibrate the Z axis. The calibration duration will last 60 s or more. An effective capture will turn the color of the screen green.</li>
	<li>Click Finish to complete the calibration.</li>
	<li>Click Save Calibration to store the effective calibration mode. 
	NOTE: The calibration is performed in a 2D model; a 3D model is often used during the test.</li>
</ol>
4. iTUG test
NOTE: The participants should wear tight but comfortable clothes and shoes.
<ol>
	<li>Attach the reflective points on the anatomical landmarks: cervical 7 spinous process andthoracic 10 spinous process, left acromion and right acromion. 
	NOTE: Each participant could have up to 17 reflective points attached. The more reflective points that are attached, the more accurate the data collected, but also the less comfortable the participants feel.</li>
	<li>Right-click the reflective points at the right line of the screen and nominate them as C7, T10, left shoulder, and right shoulder.</li>
	<li>Show the instruction to the participants. The instruction is &#34;Please rise from the chair, walk 3 m at your preferred speed, turn around, return, and sit.&#34;</li>
	<li>Ask the participants to perform the iTUG test beforehand to make sure they are familiar with the instruction. 
	NOTE: Make sure each participant is comfortable after the reflective points are attached.</li>
	<li>Instruct the participant to perform the iTUG test. 
	NOTE: The participants need to walk to complete the iTUG task.</li>
	<li>While the participant is performing the iTUG test, click the start and stop buttons on the screen of the computer. 
	NOTE: During the iTUG test, the motion capture system samples data from all attached points at a frequency of 60 Hz; a video is formed accordingly (see Video 1).</li>
</ol>
5. Data collection and definition of iTUG test variables
<ol>
	<li>Click the Recording Setting button. 
	NOTE: The data are stored and found in a spreadsheet.</li>
	<li>Select Raw Camera Data |Tracked ASCTI | Tracked Binary. The Capture Duration is 20 s.</li>
	<li>Type in the name of the participant.</li>
	<li>Click the Record button to start data collection.</li>
	<li>Identify sub-phases of the iTUG test by reviewing the video and calculate the variables according to the data.
	<ol>
		<li>Define the following variables: traditional TUG test total time, phase 1 time (rising from the chair), phase 1 body sway (rising from the chair), phase 2 time (walking 3 m at the preferred speed), phase 2 body sway (walking 3 m at the preferred speed), phase 3 time (turning around), phase 3 body sway (turning around), angular velocity of phase 3 (turning around), phase 4 time (returning), phase 4 body sway (returning), and phase 5 time (sitting). 
		NOTE: The details are similar to those described by Caronni and colleagues15.</li>
	</ol>
	</li>
</ol>
6. Downton Fall Risk Index (DFRI)
<ol>
	<li>Assess the falling risk using the Downton Fall Risk Index (DFRI). 
	NOTE: The DFRI (Table 1) includes 11 risk items, which are scored one point each. Scores are summed to a total index score, range 0-11. A score of &#8805;3 is considered to indicate a high risk of falls. To evaluate falling risk, DFRI is usually used for participants admitted in a hospital, and the Home Falls and Accidents Screening Tool (HOME FAST) is more suitable for community dwellers.</li>
</ol>
7. Statistical analysis
<ol>
	<li>Use a commercially available statistical software package to perform all statistical analyses. Use Student&#39;s t-test to assess group differences and choose Pearson correlation coefficient to assess the relationship between subcomponents of iTUG and DFRI score on the total sample, with P &#60; 0.05 indicating a statistically significant difference.</li>
	<li>Use the Bland-Altman procedures to assess agreement for Phase 1 duration, Phase 3 angular velocity, and Phase 4 duration between our method and the McRoberts sensor (see the Table of Materials). Calculate the mean difference between the two methods of measurement and the 95% limit of agreement for the mean difference calculated.</li>
</ol>

Evaluating the Risk of Falling in Elderly Using iTUG Motion Capture Technology

<ol>
	<li>Dokuzlar, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessment+of+factors+that+increase+risk+of+falling+in+older+women+by+four+different+clinical+methods.">Assessment of factors that increase risk of falling in older women by four different clinical methods.</a> Aging Clin Exp Res. 32 (3), 483-490 (2020).</li><li>King, B., Pecanac, K., Krupp, A., Liebzeit, D., Mahoney, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+fall+prevention+on+nurses+and+care+of+fall+risk+patients.">Impact of fall prevention on nurses and care of fall risk patients.</a> The Gerontologist. 58 (2), 331-340 (2018).</li><li>Mazur, K., Wilczy&#324;ski, K., Szewieczek, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Geriatric+falls+in+the+context+of+a+hospital+fall+prevention+program:+delirium,+low+body+mass+index,+and+other+risk+factors.">Geriatric falls in the context of a hospital fall prevention program: delirium, low body mass index, and other risk factors.</a> Clin Interv Aging. 11, 1253-1261 (2016).</li><li>Beck Jepsen, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+falls+in+older+adults:+an+umbrella+review+of+instruments+assessing+gait,+balance,+and+functional+mobility.">Predicting falls in older adults: an umbrella review of instruments assessing gait, balance, and functional mobility.</a> BMC Geriatrics. 22 (1), 615 (2022).</li><li>Meekes, W. M., Korevaar, J. C., Leemrijse, C. J., Van de Goor, I. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Practical+and+validated+tool+to+assess+falls+risk+in+the+primary+care+setting:+a+systematic+review.">Practical and validated tool to assess falls risk in the primary care setting: a systematic review.</a> BMJ Open. 11 (9), 045431 (2021).</li><li>Mirelman, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Association+between+performance+on+Timed+Up+and+Go+subtasks+and+mild+cognitive+impairment:+further+insights+into+the+links+between+cognitive+and+motor+function.">Association between performance on Timed Up and Go subtasks and mild cognitive impairment: further insights into the links between cognitive and motor function.</a> J Am Geriatr Soc. 62 (4), 673-678 (2014).</li><li>Palmerini, L., Mellone, S., Avanzolini, G., Valzania, F., Chiari, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantification+of+motor+impairment+in+Parkinson's+disease+using+an+instrumented+timed+up+and+go+test.">Quantification of motor impairment in Parkinson's disease using an instrumented timed up and go test.</a> IEEE Trans Neural Syst Rehabil Eng. 21 (4), 664-673 (2013).</li><li>Cimolin, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Do+wearable+sensors+add+meaningful+information+to+the+Timed+Up+and+Go+test?+A+study+on+obese+women.">Do wearable sensors add meaningful information to the Timed Up and Go test? A study on obese women.</a> J Electromyogr Kinesiol. 44, 78-85 (2019).</li><li>Ayena, J. C., Chioukh, L., Otis, M. J., Deslandes, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Risk+of+falling+in+a+timed+up+and+go+test+using+an+UWB+Radar+and+an+instrumented+insole.">Risk of falling in a timed up and go test using an UWB Radar and an instrumented insole.</a> Sensors. 21 (3), 722 (2021).</li><li>Sample, R. B., Kinney, A. L., Jackson, K., Diestelkamp, W., Bigelow, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+key+outcome+measures+when+using+the+instrumented+timed+up+and+go+and/or+posturography+for+fall+screening.">Identification of key outcome measures when using the instrumented timed up and go and/or posturography for fall screening.</a> Gait Posture. 57, 168-171 (2017).</li><li>Beyea, J., McGibbon, C. A., Sexton, A., Noble, J., O'Connell, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Convergent+validity+of+a+wearable+sensor+system+for+measuring+sub-task+performance+during+the+timed+up-and-go+test.">Convergent validity of a wearable sensor system for measuring sub-task performance during the timed up-and-go test.</a> Sensors. 17 (4), 934 (2017).</li><li>McGough, E. L., Gries, M., Teri, L., Kelly, V. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validity+of+instrumented+360&#176;+turn+test+in+older+adults+with+cognitive+impairment.">Validity of instrumented 360&#176; turn test in older adults with cognitive impairment.</a> Phys Occup Ther Geriatr. 38 (2), 170-184 (2020).</li><li>Wang, H., Brown, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effects+of+total+ankle+replacement+on+ankle+joint+mechanics+during+walking.">The effects of total ankle replacement on ankle joint mechanics during walking.</a> J Sport Health Sci. 6 (3), 340-345 (2017).</li><li>Mehdizadeh, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Toronto+older+adults+gait+archive:+video+and+3D+inertial+motion+capture+data+of+older+adults'+walking.">The Toronto older adults gait archive: video and 3D inertial motion capture data of older adults' walking.</a> Sci Data. 9 (1), 398 (2022).</li><li>Caronni, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Criterion+validity+of+the+instrumented+Timed+Up+and+Go+test:+A+partial+least+square+regression+study.">Criterion validity of the instrumented Timed Up and Go test: A partial least square regression study.</a> Gait Posture. 61, 287-293 (2018).</li><li>Mojtaba, M., Alinaghizadeh, H., Rydwik, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Downton+Fall+Risk+Index+during+hospitalisation+is+associated+with+fall-related+injuries+after+discharge:+a+longitudinal+observational+study.">Downton Fall Risk Index during hospitalisation is associated with fall-related injuries after discharge: a longitudinal observational study.</a> J Physiother. 64 (3), 172-177 (2018).</li><li>Hershkovitz, L., Malcay, O., Grinberg, Y., Berkowitz, S., Kalron, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+contribution+of+the+instrumented+Timed-Up-and-Go+test+to+detect+falls+and+fear+of+falling+in+people+with+multiple+sclerosis.">The contribution of the instrumented Timed-Up-and-Go test to detect falls and fear of falling in people with multiple sclerosis.</a> Mult Scler Relat Disord. 27, 226-231 (2019).</li><li>Zarzeczny, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aging+effect+on+the+instrumented+Timed-Up-and-Go+test+variables+in+nursing+home+women+aged+80-93+years.">Aging effect on the instrumented Timed-Up-and-Go test variables in nursing home women aged 80-93 years.</a> Biogerontology. 18 (4), 651-663 (2017).</li><li>Kim, K. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+instrumented+timed+&amp;#34;Up+&amp;+Go&amp;#34;+test+distinguishes+turning+characteristics+in+vestibular+hypofunction.">The instrumented timed &amp;#34;Up &amp; Go&amp;#34; test distinguishes turning characteristics in vestibular hypofunction.</a> Phys Ther. 101 (7), 103 (2021).</li><li>Montero-Odasso, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=World+guidelines+for+falls+prevention+and+management+for+older+adults:+a+global+initiative.">World guidelines for falls prevention and management for older adults: a global initiative.</a> Age Ageing. 51 (9), (2023).</li><li>Rocchi, L., Palmerini, L., Weiss, A., Herman, T., Hausdorff, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Balance+testing+with+inertial+sensors+in+patients+with+Parkinson's+disease:+assessment+of+motor+subtypes.">Balance testing with inertial sensors in patients with Parkinson's disease: assessment of motor subtypes.</a> IEEE Trans Neural Syst Rehabil Eng. 22 (5), 1064-1071 (2014).</li><li>Ngo, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparative+severity+assessment+of+impaired+balance+due+to+cerebellar+ataxia+using+regression+models.">A comparative severity assessment of impaired balance due to cerebellar ataxia using regression models.</a> Annu Int Conf IEEE Eng Med Biol Soc. 2020, 4571-4574 (2020).</li><li>Celik, Y., Powell, D., Woo, W. L., Stuart, S., Godfrey, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+feasibility+study+towards+instrumentation+of+the+Sport+Concussion+Assessment+Tool+(iSCAT).">A feasibility study towards instrumentation of the Sport Concussion Assessment Tool (iSCAT).</a> Annu Int Conf IEEE Eng Med Biol Soc. 2020, 4624-4627 (2020).</li><li>Coni, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+standard+clinical+and+instrumented+physical+performance+tests+in+discriminating+functional+status+of+high-functioning+people+aged+6170+years+old.">Comparison of standard clinical and instrumented physical performance tests in discriminating functional status of high-functioning people aged 6170 years old.</a> Sensors. 19 (3), 449 (2019).</li></ol>

The authors have no conflicts of interest to disclose.

The critical steps in the protocol are to attach the reflective points accurately to the anatomical landmarks to avoid bias. Furthermore, the identification of each subcomponent of the iTUG test is also a critical step; a video review is helpful for the identification.
A marginal difference existed between groups in the TUG test scores implying that traditional TUG scores might not be sensitive enough to discriminate risk of falling. We did not find obvious differences between the groups in Ph...

Falling is one of the most common geriatric syndromes and is the second leading cause of accidental or unintentional injury-related deaths worldwide1. In adults aged above 65 years, falling can result in functional impairment, disability, decreased quality of life, increased length of stay in hospitals, and even mortality2,3. Therefore, preventing falls is of utmost importance.
To determine predictors of fall events, previous researchers have focused on gait analyses, balance tests, mental state, sedative drug use, as well as history of falling in the preceding year4,5 The Timed Up and Go (TUG) test is a quick performance-based measure of mobility. The TUG test has been extensively studied in older adults and is recommended as a simple screening test for the risk of falls6. This widely used test consists of rising from a chair, walking 3 m at the preferred speed, turning around, returning, and sitting. The traditional clinical outcome of this test depends on its total duration7 and is assessed by a stopwatch.
In clinical practice, the conventional TUG test measures the total time to perform a series of activities without dividing the performance of the subject into subcomponents8. Recently, some investigators have proposed that different TUG test components might be particularly sensitive as predictors of future falls9. When using the digitized instrumented TUG (iTUG) test, the individual components of the TUG test can be analyzed separately. By using the iTUG, it is possible to objectively evaluate several characteristics of each TUG test sub-phase and obtain quantitative data, such as the relevant durations, velocities, and angular velocity of each movement. With more detailed data, the iTUG test has shown the advantage of detecting specific deficits that may be more indicative of the fall risk10.
As the gold standard in movement analysis, motion capture technologies have been used to detect movement in patients with Parkinson&#39;s Disease (PD)11, cognitive impairment12, and ankle arthritis13, as well as in healthy adults14. In the current study, we aimed to analyze the iTUG test subcomponents using motion capture technology and to determine the correlation between iTUG test subtasks and the potential risk of future falls.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Black strip</td>
			<td>Deli</td>
			<td>60 mm x 20 m</td>
			<td></td>
		</tr>
		<tr>
			<td>Calibrator</td>
			<td>NOKOV</td>
			<td>reflector marker1</td>
			<td>L shape</td>
		</tr>
		<tr>
			<td>Calibrator</td>
			<td>NOKOV</td>
			<td>reflector marker2</td>
			<td>T shape</td>
		</tr>
		<tr>
			<td>Chair</td>
			<td>YUANSHENGYUANDAI</td>
			<td>&#8220;10076062317820&#8221;</td>
			<td></td>
		</tr>
		<tr>
			<td>Computer</td>
			<td>HUAWEI</td>
			<td>HONOR</td>
			<td></td>
		</tr>
		<tr>
			<td>McRoberts sensor&#160;</td>
			<td colspan="3">DynaPort Hybrid, McRoberts, The Hague, The Netherland</td>
		</tr>
		<tr>
			<td>Motion capture camera</td>
			<td>NOKOV</td>
			<td>Mars2H</td>
			<td></td>
		</tr>
		<tr>
			<td>Motion capture software</td>
			<td>NOKOV</td>
			<td>DG-01</td>
			<td></td>
		</tr>
		<tr>
			<td>Reflective marker</td>
			<td>NOKOV</td>
			<td>small marker</td>
			<td>for calibrators</td>
		</tr>
		<tr>
			<td>Reflective marker</td>
			<td>NOKOV</td>
			<td>large marker</td>
			<td>for participants</td>
		</tr>
	</tbody>
</table>

using motion capture technology in the instrumented timed up and go test to detect the risk of falling in aged adults

Despite efforts made by medicine and technology, the incidence of falls in older adults is still increasing. Therefore, early detection of the falling risk is becoming increasingly important for falling prevention. The Timed Up and Go (TUG) test is a well-accepted tool to assess mobility and can be used in predicting future fall risk in aged adults. In clinical practice, the total time to complete the test is the main outcome measure of the TUG test. Owing to its simplicity and generality, the traditional TUG test has been considered a global test for movement analysis. However, recently, researchers have attempted to split the TUG test into subcomponents and have updated its score system for further investigations. The instrumented Time Up and Go (iTUG) test, which is a new modification of the traditional TUG test, has been reported to be a sensitive tool for predicting movement disorders and the risk of falls in older adults. The goal of the present study was to analyze the iTUG test subcomponents using motion capture technology, and to determine which iTUG test subtasks are related to the potential risk of future falls.

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Using Motion Capture Technology in the Instrumented Timed Up and Go Test to Detect the Risk of Falling in Aged Adults

In the current study, we aimed to analyze the iTUG Test sub components, using motion capture technology, and to determine the correlation between iTUG Test subtasks. We wanted to show that iTUG Test has highlighted points to show more details than traditional TUG Test. The significance of this method is that it's subdivides the traditional TUG Test, with the motion capture system.Providing new variables regarding time and body sway. Our results demonstrated that some sub-components time variables rather than body sway variables, differed between groups with different leveled risk of falling. As recent studies have evidence that the TUG test sub per competence are associated with cognitive function.This method can also play with the V for screening for cognitive impairment.

Thirteen aged participants with a high risk of falling (DFRI score: 3-11) and 11 aged subjects with a low risk of falling (DFRI score: 0-2) were recruited. The DFRI is detailed in Table 1. As has been mentioned previously, a score of 3 or more is considered to indicate a high risk of falls for patients during hospitalization16.
Demographic data are shown in Table 1, which...

The instrumented Timed Up and Go (iTUG) test is gaining increasing attention in body sway and gait analysis with the development of new technologies. We present a protocol to analyze the subcomponents of the iTUG with motion capture.

Despite efforts made by medicine and technology, the incidence of falls in older adults is still increasing. Therefore, early detection of the falling ...

using-motion-capture-technology-instrumented-timed-up-go-test-to

Research

JoVE Journal

Medicine

721 vues.  The Seventh Medical Center of Chinese PLA General Hospital. Dans la présente étude, nous avons cherché à analyser les sous-composants du test iTUG, à l’aide de la technologie de capture de mouvement, et à déterminer la corrélation entre les sous-tâches du test iTUG. Nous voulions montrer que iTUG Test a mis en évidence des points pour montrer plus de détails que le TUG Test traditionnel. L’importance de cette méthode est qu’elle subdivise le test TUG traditionnel, avec le système de capture de mouve...

Vidéo: Pleins feux sur l’auteur : Innovations dans le test iTUG pour une meilleure évaluation des risques et des connaissances cognitives

Preparation of Test Area and Software for iTUG Studies in Aged Adults

To begin, set up a standardized eye tug test area, ensuring that it is equipped with 12 cameras distributed around the room. Incorporate traditional tug setups, such as a chair and a sign to remind the participant to turn back. Make sure that the motion capture area is confined within the camera coverage and that all tug test setup materials are non-white reflective.Next, initiate the motion capture software by clicking the software icon. For general camera configuration, select the default mode. Also apply default settings for selected camera settings and camera configuration settings.Select live mode for realtime settings and click on marker sets to configure the marker settings. For the calibration of ground axis, select the XY-axis and choose millimeters in calibration units. Click on calibrate to select the calibration variation.Then, select initial calibration and click on the L-shaped button. Place the L-shaped calibrator at the center of the field to ensure visibility by all cameras. Then, check the screen to confirm that all 12 cameras detect four reflective markers.If any camera detects three or less markers, reduce lightness and threshold at the left of the screen. If any camera detects five or more markers, check the field and cover the unwanted markers. Next, click on the T-shaped button, and wave it in the field to ensure all cameras capture it.Then, select the Z-axis in calibration of up-axis and use millimeters for calibration units. Have the doctor's verify the effectiveness of camera capture by waving the markers in each corner of the field. Click on finish to complete the calibration process and click save calibration.

iTUG Tests to Evaluate Risk of Falling in Aged Adults

To begin, set up the test area and software for iTUG test. Attach reflective markers to the participant's seven cervical spinous processes, ten thoracic spinous processes, the left acromion, and the right acromion. Right click the reflective markers on the right line of the screen to name them as C7, T10, left_shoulder, and right_shoulder.Present the iTUG test instructions to the participants. Ask the participants to rise from the chair, walk three meters at their preferred speed, turn around, return, and sit. While the participant is performing the iTUG test, click on the start and pause buttons on the screen of the computer.Next, enter the participant's name in the system for data collection purposes, and press the record button to start the data recording. After recording, review the video to identify the iTUG's test sub-phases. Similarly define the phase two time, phase two body sway, phase three time, phase three body sway, angular velocity of phase three, phase four time, phase four body sway, and phase five time.Finally, evaluate the falling risk of the participant by using the Downton fall risk index. For this study, 13 aged participants with a high risk of falling and 11 participants with a low risk of falling were studied. Despite no significant differences in sex, age, or overall TUG test scores between the groups, high-risk individuals had notably longer durations in specific iTUG test phases, and lower angular velocity during turning, suggesting impaired mobility.A significant correlation was found between DFRI scores and the duration of several iTUG test phases, but not with body sway metrics, indicating specific mobility challenges correlated with fall risk.